Total Output Total Output in Manufacturing
Notes: This figure shows the qualitative theoretical evolution of total output (left panel) and total output in manufacturing (right panel) implied by our model when at time t = 0 skilled-biased-factor-augmenting technology (As) in agriculture increases. The figure displays the evolution of the economy both with (dashed line) and without (solid line) the technological change.
of soy technical change on labor allocation across industries within the manufacturing sector. Finally, in section 4.4, we focus on the impact of industrial specialization driven by soy technical change on manufacturing productivity in the long-run.
4.1 Identification Strategy
To estimate the effect of soy technical change on our outcomes of interest, we estimate the following equation:
∆Yk = α + β∆Asoyk + ϕXk+ εk (11) where ∆Yk is the change in the outcome of interest in microregion k between 2000 and 2010, ∆Asoyk corresponds to our exogenous measure of technical change in soy described in section 2.2, and Xk is a vector of controls of microregion k. Our identification strategy relies on the fact that the new GE soybeans seeds were legalized in Brazil in 2003, and that this new technology disproportionately favored microregions with certain soil and weather characteristics (as captured by ∆Asoyk ), something that was not anticipated as of 2000.
In our baseline specification, we include as controls the share of rural population in 1991 and a measure of technical change in maize. The lagged share of rural population captures differential trends in the outcome variable between urban and rural microregions, whereas the technical change in maize captures the differential impact across microregions of new maize production methods that were introduced in this period.25 In our extended
25This new production methods – and in particular second-season maize – might have affected some of the outcomes and are partially correlated with the soy shock. See Bustos et al. (2016) for a detailed discussion of second-season maize and pre-trends.
specification, we also control for the initial level of income per capita, alphabetization rate, and population density, all observed in 1991 and sourced from the Population Census.
These controls are meant to capture differential trends across microregions with different initial levels of income and human capital.
4.2 Effect of Technical Change on Labor Reallocation and Wages
In this section we start by documenting that soy technical change introduced by GE seeds was labor-saving. Microregions that could benefit more from the new technology experienced a reallocation of workers from the agricultural sector to the manufacturing and services sectors. Next, we document that soy technical change was also skill-biased. In particular, with the introduction of this new technology, high-skilled workers had relatively more opportunities in the agricultural sector than low-skilled workers. This led low-skilled workers to leave agriculture. Finally, we document the effect of this increase in low-skill labor supply on local wages.
We start in Table 3 by documenting that soy technical change generated a realloca-tion of labor from agriculture into manufacturing, i.e. it led to structural transformarealloca-tion.
We find that microregions with higher exposure to soy technical change experienced a decrease in the share of workers employed in agriculture and an increase in the share of workers employed in manufacturing and services. Notice that – as shown in column (2) – soy technical change had only small and not significant effects on total employ-ment. Thus, the employment changes that we document in what follows are not driven by migration between microregions or by changes in the total number of workers em-ployed, but by movement of workers across sectors within microregions.26 The estimate presented in column (4) indicates that microregions with a one standard deviation larger increase in soy technical change experienced a 2.4 percentage points lower change in agri-cultural employment share. This estimate is stable to the inclusion of controls. These agricultural workers displaced by the new soy technology relocated into manufacturing and services. Manufacturing employment shares increased by 1.7 percentage points – and services employment share by 0.7 percentage points for a standard deviation difference in soy technical change –, hence absorbing the bulk of workers released from agriculture. In sum, the results presented in Table 3 indicate that soy technical change was labor-saving and led to structural transformation, which are the main findings documented in Bustos et al. (2016).27
26In Table A2 in the Appendix we provide direct evidence on the lack of internal migration responses.
27Bustos et al. (2016) find that soy technical change had a positive and significant effect on the em-ployment share in manufacturing but no significant effect on the emem-ployment share in the services sector.
Table 3 in this paper documents that microregions more exposed to soy technical change experienced an increase in employment share in both manufacturing and services. There are two reasons behind this difference in results when the outcome is the employment share in the services sector. The first is that, in this paper, we focus on remunerated labor – i.e. workers receiving a wage – whereas Bustos et al.
Table 3: Effect of technical change in soy on employment shares
(1) (2) (3) (4) (5) (6) (7) (8)
VARIABLES ∆ Log. L ∆ Log. L ∆LLa ∆LLa ∆LLm ∆LLm ∆LLs ∆LLs
∆Asoy -0.033** -0.011 -0.034*** -0.033*** 0.020*** 0.023*** 0.014*** 0.009**
[0.015] [0.013] [0.005] [0.005] [0.004] [0.005] [0.005] [0.004]
Observations 557 557 557 557 557 557 557 557
R-squared 0.023 0.154 0.218 0.242 0.086 0.107 0.251 0.311
Baseline Controls Yes Yes Yes Yes Yes Yes Yes Yes
All Controls No Yes No Yes No Yes No Yes
Notes: Changes in dependent variables are calculated over the years 2000 and 2010 (source: Population Censuses). The unit of observation is the micro-region. All the regressions include the baseline specification controls which are the share of rural population in 1991 and a measure of technical change in maize. The regressions with all controls also include income per capita (in logs), population density (in logs), literacy rate, all observed in the 1991 Population Census. Robust standard errors reported in brackets. Significance levels:∗∗∗p < 0.01,∗∗p < 0.05,∗p < 0.1.
Next, in Table 4, we study the effect of soy technical change on the reallocation across sectors of workers with different skills. More specifically, we characterize whether the reallocation of workers from agriculture to manufacturing documented in Table 3 is mostly driven by unskilled or skilled workers.
In Panel A of Table 4 we focus on unskilled workers. Columns (1) and (2) show that soy technical change had a negative – although not precisely estimated – effect on the total number of low-skilled workers. Then, in columns (3) to (8), we study the effect of soy technical change on the share of low-skilled workers employed in each sector. We find that microregions more exposed to soy technical change experienced a reallocation of unskilled workers from agriculture to manufacturing. The magnitude of the estimated coefficients indicate that microregions with a standard deviation higher increase in soy technical change experienced a 2.4 percentage points larger decrease in the share of low-skilled workers employed in agriculture, and a corresponding 2.1 percentage points larger increase in the share of low-skilled workers employed in manufacturing. These magnitudes correspond to a 7.2 percent decrease in the initial share of low-skilled workers employed in agriculture, and a 15 percent increase of the share of those employed in manufacturing.
Combined with the coefficient presented in column (2), these results are consistent with a decline in the absolute demand for low-skilled labor in agriculture in response to skilled labor-augmenting technical change, as predicted by the model. The low-skilled employees released from agriculture moved primarily into manufacturing.
In Panel B we focus instead on skilled workers. We find that microregions more ex-posed to soy technical change experienced a higher increase in the total number of high-skill workers, as shown in Columns (1) and (2).28 Columns (3) to (8) report the effect of
(2016) also included workers who helped household members without receiving a payment or worked in subsistence agriculture. The second is the unit of observation, which is a microregion in Table 3, a municipality in Bustos et al. (2016).
28As we document in Table A2 in the Appendix, this differential increase in high-skill workers is not
soy technical change on the share of high-skilled workers by sector of employment. We find that microregions more exposed to soy technical change experienced a larger decrease in the share of high-skill workers in agriculture.29 We also find that microregions more exposed to soy technical change experienced a larger increase in the share of high-skill workers employed in manufacturing, consistently with some complementarity in the use of both types of workers. The magnitude of the estimated coefficients indicate that microre-gions with one standard deviation higher increase in soy technical change experienced a 1.2 percentage points larger decrease in the share of high-skilled workers employed in agriculture (10 percent of their initial share), and a corresponding 1 percentage points increase in the share of high-skilled workers employed in manufacturing (5.8 percent of their initial share).